Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations12811
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory120.0 B

Variable types

Numeric13
Categorical5

Alerts

Alpha1 is highly overall correlated with Alpha2 and 5 other fieldsHigh correlation
Alpha2 is highly overall correlated with Alpha1 and 6 other fieldsHigh correlation
Beta1 is highly overall correlated with Alpha1 and 6 other fieldsHigh correlation
Beta2 is highly overall correlated with Alpha1 and 5 other fieldsHigh correlation
Delta is highly overall correlated with Alpha1 and 4 other fieldsHigh correlation
Gamma1 is highly overall correlated with Alpha1 and 6 other fieldsHigh correlation
Gamma2 is highly overall correlated with Alpha2 and 3 other fieldsHigh correlation
SubjectID is highly overall correlated with age and 2 other fieldsHigh correlation
Theta is highly overall correlated with Alpha1 and 5 other fieldsHigh correlation
VideoID is highly overall correlated with predefinedlabelHigh correlation
age is highly overall correlated with SubjectID and 2 other fieldsHigh correlation
ethnicity is highly overall correlated with SubjectID and 1 other fieldsHigh correlation
gender is highly overall correlated with SubjectID and 1 other fieldsHigh correlation
predefinedlabel is highly overall correlated with VideoIDHigh correlation
SubjectID has 1261 (9.8%) zeros Zeros
VideoID has 1181 (9.2%) zeros Zeros
Attention has 1423 (11.1%) zeros Zeros
Mediation has 1423 (11.1%) zeros Zeros

Reproduction

Analysis started2024-11-26 05:58:42.968689
Analysis finished2024-11-26 05:59:02.837527
Duration19.87 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

SubjectID
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4873936
Minimum0
Maximum9
Zeros1261
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size50.2 KiB
2024-11-25T21:59:02.900772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8653726
Coefficient of variation (CV)0.63853828
Kurtosis-1.2221488
Mean4.4873936
Median Absolute Deviation (MAD)2
Skewness0.0096318925
Sum57488
Variance8.2103602
MonotonicityIncreasing
2024-11-25T21:59:03.009026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 1314
10.3%
1 1301
10.2%
4 1295
10.1%
2 1284
10.0%
8 1282
10.0%
7 1276
10.0%
6 1275
10.0%
5 1262
9.9%
0 1261
9.8%
9 1261
9.8%
ValueCountFrequency (%)
0 1261
9.8%
1 1301
10.2%
2 1284
10.0%
3 1314
10.3%
4 1295
10.1%
5 1262
9.9%
6 1275
10.0%
7 1276
10.0%
8 1282
10.0%
9 1261
9.8%
ValueCountFrequency (%)
9 1261
9.8%
8 1282
10.0%
7 1276
10.0%
6 1275
10.0%
5 1262
9.9%
4 1295
10.1%
3 1314
10.3%
2 1284
10.0%
1 1301
10.2%
0 1261
9.8%

VideoID
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5752088
Minimum0
Maximum9
Zeros1181
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size50.2 KiB
2024-11-25T21:59:03.099587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8422524
Coefficient of variation (CV)0.621229
Kurtosis-1.2093625
Mean4.5752088
Median Absolute Deviation (MAD)2
Skewness-0.03706566
Sum58613
Variance8.0783986
MonotonicityNot monotonic
2024-11-25T21:59:03.194191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 1414
11.0%
8 1412
11.0%
2 1356
10.6%
4 1281
10.0%
7 1280
10.0%
6 1274
9.9%
9 1230
9.6%
1 1206
9.4%
0 1181
9.2%
3 1177
9.2%
ValueCountFrequency (%)
0 1181
9.2%
1 1206
9.4%
2 1356
10.6%
3 1177
9.2%
4 1281
10.0%
5 1414
11.0%
6 1274
9.9%
7 1280
10.0%
8 1412
11.0%
9 1230
9.6%
ValueCountFrequency (%)
9 1230
9.6%
8 1412
11.0%
7 1280
10.0%
6 1274
9.9%
5 1414
11.0%
4 1281
10.0%
3 1177
9.2%
2 1356
10.6%
1 1206
9.4%
0 1181
9.2%

Attention
Real number (ℝ)

Zeros 

Distinct61
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.313871
Minimum0
Maximum100
Zeros1423
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-11-25T21:59:03.315613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q127
median43
Q357
95-th percentile78
Maximum100
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation23.152953
Coefficient of variation (CV)0.56041598
Kurtosis-0.52728153
Mean41.313871
Median Absolute Deviation (MAD)16
Skewness-0.1688562
Sum529272
Variance536.05924
MonotonicityNot monotonic
2024-11-25T21:59:03.449444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1423
 
11.1%
43 407
 
3.2%
41 396
 
3.1%
38 392
 
3.1%
48 390
 
3.0%
44 385
 
3.0%
51 385
 
3.0%
40 379
 
3.0%
47 367
 
2.9%
54 358
 
2.8%
Other values (51) 7929
61.9%
ValueCountFrequency (%)
0 1423
11.1%
1 88
 
0.7%
3 17
 
0.1%
4 42
 
0.3%
7 29
 
0.2%
8 54
 
0.4%
10 62
 
0.5%
11 74
 
0.6%
13 84
 
0.7%
14 119
 
0.9%
ValueCountFrequency (%)
100 42
0.3%
97 14
 
0.1%
96 8
 
0.1%
94 17
 
0.1%
93 23
0.2%
91 36
0.3%
90 34
0.3%
88 40
0.3%
87 47
0.4%
84 56
0.4%

Mediation
Real number (ℝ)

Zeros 

Distinct61
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.182656
Minimum0
Maximum100
Zeros1423
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-11-25T21:59:03.578305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q137
median51
Q363
95-th percentile78
Maximum100
Range100
Interquartile range (IQR)26

Descriptive statistics

Standard deviation22.655976
Coefficient of variation (CV)0.48017594
Kurtosis-0.023759349
Mean47.182656
Median Absolute Deviation (MAD)13
Skewness-0.67986376
Sum604457
Variance513.29325
MonotonicityNot monotonic
2024-11-25T21:59:03.707288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1423
 
11.1%
51 502
 
3.9%
56 500
 
3.9%
48 476
 
3.7%
53 469
 
3.7%
54 455
 
3.6%
57 454
 
3.5%
50 454
 
3.5%
47 436
 
3.4%
63 426
 
3.3%
Other values (51) 7216
56.3%
ValueCountFrequency (%)
0 1423
11.1%
1 22
 
0.2%
3 6
 
< 0.1%
4 14
 
0.1%
7 13
 
0.1%
8 16
 
0.1%
10 18
 
0.1%
11 21
 
0.2%
13 25
 
0.2%
14 47
 
0.4%
ValueCountFrequency (%)
100 23
 
0.2%
97 9
 
0.1%
96 8
 
0.1%
94 19
 
0.1%
93 19
 
0.1%
91 25
 
0.2%
90 39
0.3%
88 44
0.3%
87 60
0.5%
84 78
0.6%

Raw
Real number (ℝ)

Distinct1104
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.57076
Minimum-2048
Maximum2047
Zeros57
Zeros (%)0.4%
Negative3939
Negative (%)30.7%
Memory size100.2 KiB
2024-11-25T21:59:03.833581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2048
5-th percentile-300
Q1-14
median35
Q390
95-th percentile1371
Maximum2047
Range4095
Interquartile range (IQR)104

Descriptive statistics

Standard deviation597.92104
Coefficient of variation (CV)9.1187145
Kurtosis7.6600038
Mean65.57076
Median Absolute Deviation (MAD)53
Skewness0.2696119
Sum840027
Variance357509.56
MonotonicityNot monotonic
2024-11-25T21:59:03.958080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2047 501
 
3.9%
-2048 382
 
3.0%
38 109
 
0.9%
35 103
 
0.8%
53 103
 
0.8%
33 101
 
0.8%
43 100
 
0.8%
57 96
 
0.7%
27 96
 
0.7%
42 95
 
0.7%
Other values (1094) 11125
86.8%
ValueCountFrequency (%)
-2048 382
3.0%
-2047 2
 
< 0.1%
-2043 1
 
< 0.1%
-2039 1
 
< 0.1%
-2028 2
 
< 0.1%
-2004 1
 
< 0.1%
-1998 1
 
< 0.1%
-1978 1
 
< 0.1%
-1958 1
 
< 0.1%
-1953 1
 
< 0.1%
ValueCountFrequency (%)
2047 501
3.9%
2046 1
 
< 0.1%
2045 1
 
< 0.1%
2044 2
 
< 0.1%
2043 1
 
< 0.1%
2042 1
 
< 0.1%
2041 1
 
< 0.1%
2039 1
 
< 0.1%
2037 3
 
< 0.1%
2034 1
 
< 0.1%

Delta
Real number (ℝ)

High correlation 

Distinct12280
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean605785.26
Minimum448
Maximum3964663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-11-25T21:59:04.080205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum448
5-th percentile6086.5
Q198064
median395487
Q3916623
95-th percentile1945866
Maximum3964663
Range3964215
Interquartile range (IQR)818559

Descriptive statistics

Standard deviation637623.56
Coefficient of variation (CV)1.0525571
Kurtosis1.8460076
Mean605785.26
Median Absolute Deviation (MAD)355506
Skewness1.395603
Sum7.760715 × 109
Variance4.0656381 × 1011
MonotonicityNot monotonic
2024-11-25T21:59:04.215962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3660 3
 
< 0.1%
6904 3
 
< 0.1%
574519 3
 
< 0.1%
602523 3
 
< 0.1%
825027 3
 
< 0.1%
445929 2
 
< 0.1%
1705012 2
 
< 0.1%
157030 2
 
< 0.1%
9428 2
 
< 0.1%
191050 2
 
< 0.1%
Other values (12270) 12786
99.8%
ValueCountFrequency (%)
448 1
< 0.1%
463 1
< 0.1%
480 1
< 0.1%
482 1
< 0.1%
536 1
< 0.1%
633 1
< 0.1%
676 1
< 0.1%
861 1
< 0.1%
889 1
< 0.1%
902 1
< 0.1%
ValueCountFrequency (%)
3964663 1
< 0.1%
3958185 1
< 0.1%
3913892 1
< 0.1%
3777001 1
< 0.1%
3732103 1
< 0.1%
3726189 1
< 0.1%
3704955 1
< 0.1%
3679817 1
< 0.1%
3674575 1
< 0.1%
3654692 1
< 0.1%

Theta
Real number (ℝ)

High correlation 

Distinct12070
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168052.6
Minimum17
Maximum3007802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-11-25T21:59:04.354948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile7197
Q126917.5
median81331
Q3205276
95-th percentile619673.5
Maximum3007802
Range3007785
Interquartile range (IQR)178358.5

Descriptive statistics

Standard deviation244134.57
Coefficient of variation (CV)1.4527271
Kurtosis17.141761
Mean168052.6
Median Absolute Deviation (MAD)65031
Skewness3.4689347
Sum2.1529219 × 109
Variance5.9601688 × 1010
MonotonicityNot monotonic
2024-11-25T21:59:04.481471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116851 4
 
< 0.1%
41729 4
 
< 0.1%
16494 3
 
< 0.1%
67143 3
 
< 0.1%
72675 3
 
< 0.1%
12595 3
 
< 0.1%
33117 3
 
< 0.1%
15732 3
 
< 0.1%
6516 3
 
< 0.1%
111295 3
 
< 0.1%
Other values (12060) 12779
99.8%
ValueCountFrequency (%)
17 1
< 0.1%
25 1
< 0.1%
121 1
< 0.1%
694 1
< 0.1%
1068 1
< 0.1%
1074 1
< 0.1%
1095 1
< 0.1%
1136 1
< 0.1%
1172 1
< 0.1%
1202 1
< 0.1%
ValueCountFrequency (%)
3007802 1
< 0.1%
2567643 1
< 0.1%
2474642 1
< 0.1%
2417589 1
< 0.1%
2321736 1
< 0.1%
2260305 1
< 0.1%
2246933 1
< 0.1%
2145701 1
< 0.1%
2145547 1
< 0.1%
2085085 1
< 0.1%

Alpha1
Real number (ℝ)

High correlation 

Distinct11140
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41384.351
Minimum2
Maximum1369955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-11-25T21:59:04.604282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1895
Q16838
median17500
Q344779.5
95-th percentile159805.5
Maximum1369955
Range1369953
Interquartile range (IQR)37941.5

Descriptive statistics

Standard deviation72430.815
Coefficient of variation (CV)1.7501982
Kurtosis51.795907
Mean41384.351
Median Absolute Deviation (MAD)13016
Skewness5.5575303
Sum5.3017492 × 108
Variance5.246223 × 109
MonotonicityNot monotonic
2024-11-25T21:59:04.733296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13869 5
 
< 0.1%
4303 4
 
< 0.1%
908 4
 
< 0.1%
5752 4
 
< 0.1%
13522 4
 
< 0.1%
4028 4
 
< 0.1%
8899 4
 
< 0.1%
41555 4
 
< 0.1%
1830 4
 
< 0.1%
2997 4
 
< 0.1%
Other values (11130) 12770
99.7%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
25 1
< 0.1%
73 1
< 0.1%
75 1
< 0.1%
77 2
< 0.1%
91 1
< 0.1%
134 1
< 0.1%
146 1
< 0.1%
162 1
< 0.1%
ValueCountFrequency (%)
1369955 1
< 0.1%
1317733 1
< 0.1%
1143171 1
< 0.1%
1089428 1
< 0.1%
992457 1
< 0.1%
908710 1
< 0.1%
878352 1
< 0.1%
853451 1
< 0.1%
850147 2
< 0.1%
843805 1
< 0.1%

Alpha2
Real number (ℝ)

High correlation 

Distinct10910
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33183.393
Minimum2
Maximum1016913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-11-25T21:59:04.868955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2264
Q16852
median14959
Q334550.5
95-th percentile125765
Maximum1016913
Range1016911
Interquartile range (IQR)27698.5

Descriptive statistics

Standard deviation58314.101
Coefficient of variation (CV)1.7573278
Kurtosis50.355536
Mean33183.393
Median Absolute Deviation (MAD)10120
Skewness5.7190443
Sum4.2511245 × 108
Variance3.4005343 × 109
MonotonicityNot monotonic
2024-11-25T21:59:04.999402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3591 6
 
< 0.1%
7625 5
 
< 0.1%
3549 5
 
< 0.1%
10571 4
 
< 0.1%
6620 4
 
< 0.1%
6959 4
 
< 0.1%
11556 4
 
< 0.1%
14947 4
 
< 0.1%
2466 4
 
< 0.1%
7276 4
 
< 0.1%
Other values (10900) 12767
99.7%
ValueCountFrequency (%)
2 1
< 0.1%
10 1
< 0.1%
167 1
< 0.1%
200 1
< 0.1%
205 1
< 0.1%
247 2
< 0.1%
298 2
< 0.1%
317 1
< 0.1%
330 1
< 0.1%
353 1
< 0.1%
ValueCountFrequency (%)
1016913 1
< 0.1%
866385 1
< 0.1%
854767 1
< 0.1%
851452 1
< 0.1%
812503 1
< 0.1%
786017 1
< 0.1%
769206 1
< 0.1%
722970 1
< 0.1%
721342 1
< 0.1%
710261 1
< 0.1%

Beta1
Real number (ℝ)

High correlation 

Distinct10583
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24318.369
Minimum3
Maximum1067778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-11-25T21:59:05.520169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile2208.5
Q16140
median12818
Q327406
95-th percentile81317
Maximum1067778
Range1067775
Interquartile range (IQR)21266

Descriptive statistics

Standard deviation38379.685
Coefficient of variation (CV)1.5782179
Kurtosis97.052498
Mean24318.369
Median Absolute Deviation (MAD)8273
Skewness6.9014649
Sum3.1154262 × 108
Variance1.4730002 × 109
MonotonicityNot monotonic
2024-11-25T21:59:05.648981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5171 6
 
< 0.1%
4331 6
 
< 0.1%
18938 5
 
< 0.1%
9773 5
 
< 0.1%
4612 5
 
< 0.1%
3130 5
 
< 0.1%
3406 5
 
< 0.1%
2266 4
 
< 0.1%
6365 4
 
< 0.1%
3536 4
 
< 0.1%
Other values (10573) 12762
99.6%
ValueCountFrequency (%)
3 2
< 0.1%
157 1
< 0.1%
200 1
< 0.1%
207 1
< 0.1%
248 1
< 0.1%
254 1
< 0.1%
268 1
< 0.1%
279 1
< 0.1%
286 1
< 0.1%
289 1
< 0.1%
ValueCountFrequency (%)
1067778 1
< 0.1%
840994 1
< 0.1%
728671 1
< 0.1%
621353 1
< 0.1%
550382 1
< 0.1%
493346 1
< 0.1%
473048 1
< 0.1%
426485 1
< 0.1%
424108 1
< 0.1%
420782 1
< 0.1%

Beta2
Real number (ℝ)

High correlation 

Distinct10936
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38144.33
Minimum2
Maximum1645369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-11-25T21:59:05.774234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2792.5
Q17358.5
median15810
Q335494
95-th percentile145382
Maximum1645369
Range1645367
Interquartile range (IQR)28135.5

Descriptive statistics

Standard deviation79066.056
Coefficient of variation (CV)2.0728128
Kurtosis84.398796
Mean38144.33
Median Absolute Deviation (MAD)10415
Skewness7.332437
Sum4.8866702 × 108
Variance6.2514413 × 109
MonotonicityNot monotonic
2024-11-25T21:59:05.903113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18233 5
 
< 0.1%
3926 5
 
< 0.1%
4436 5
 
< 0.1%
13860 5
 
< 0.1%
4704 5
 
< 0.1%
15745 4
 
< 0.1%
5975 4
 
< 0.1%
11602 4
 
< 0.1%
21231 4
 
< 0.1%
3961 4
 
< 0.1%
Other values (10926) 12766
99.6%
ValueCountFrequency (%)
2 1
< 0.1%
4 1
< 0.1%
130 1
< 0.1%
183 1
< 0.1%
253 1
< 0.1%
304 1
< 0.1%
406 1
< 0.1%
431 1
< 0.1%
504 1
< 0.1%
519 1
< 0.1%
ValueCountFrequency (%)
1645369 1
< 0.1%
1573045 1
< 0.1%
1369260 1
< 0.1%
1343525 1
< 0.1%
1244467 1
< 0.1%
1221186 1
< 0.1%
1186654 1
< 0.1%
1160359 1
< 0.1%
1134229 1
< 0.1%
1083461 1
< 0.1%

Gamma1
Real number (ℝ)

High correlation 

Distinct10197
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29592.553
Minimum1
Maximum1972506
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-11-25T21:59:06.033616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1312.5
Q14058
median9763
Q324888
95-th percentile111699.5
Maximum1972506
Range1972505
Interquartile range (IQR)20830

Descriptive statistics

Standard deviation79826.367
Coefficient of variation (CV)2.6975154
Kurtosis181.88145
Mean29592.553
Median Absolute Deviation (MAD)6980
Skewness10.897155
Sum3.7911019 × 108
Variance6.3722489 × 109
MonotonicityNot monotonic
2024-11-25T21:59:06.170471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2986 5
 
< 0.1%
2466 5
 
< 0.1%
2389 5
 
< 0.1%
2447 5
 
< 0.1%
4243 5
 
< 0.1%
987 5
 
< 0.1%
3099 5
 
< 0.1%
7453 5
 
< 0.1%
2286 5
 
< 0.1%
4545 5
 
< 0.1%
Other values (10187) 12761
99.6%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
53 1
< 0.1%
83 1
< 0.1%
119 1
< 0.1%
120 1
< 0.1%
136 1
< 0.1%
139 1
< 0.1%
145 1
< 0.1%
168 1
< 0.1%
ValueCountFrequency (%)
1972506 1
< 0.1%
1856755 1
< 0.1%
1796532 1
< 0.1%
1741005 1
< 0.1%
1696053 1
< 0.1%
1603495 1
< 0.1%
1582437 1
< 0.1%
1538471 1
< 0.1%
1497096 1
< 0.1%
1364640 1
< 0.1%

Gamma2
Real number (ℝ)

High correlation 

Distinct8901
Distinct (%)69.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14415.973
Minimum2
Maximum1348117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-11-25T21:59:06.309257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile632
Q12167.5
median5116
Q312669.5
95-th percentile60828.5
Maximum1348117
Range1348115
Interquartile range (IQR)10502

Descriptive statistics

Standard deviation36035.232
Coefficient of variation (CV)2.499674
Kurtosis291.16325
Mean14415.973
Median Absolute Deviation (MAD)3656
Skewness12.559633
Sum1.8468303 × 108
Variance1.298538 × 109
MonotonicityNot monotonic
2024-11-25T21:59:06.446778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
937 8
 
0.1%
1240 8
 
0.1%
408 7
 
0.1%
1646 7
 
0.1%
1308 7
 
0.1%
1006 7
 
0.1%
2232 6
 
< 0.1%
851 6
 
< 0.1%
3229 6
 
< 0.1%
2381 6
 
< 0.1%
Other values (8891) 12743
99.5%
ValueCountFrequency (%)
2 1
< 0.1%
5 1
< 0.1%
38 1
< 0.1%
39 1
< 0.1%
50 1
< 0.1%
52 1
< 0.1%
57 1
< 0.1%
60 1
< 0.1%
65 1
< 0.1%
70 1
< 0.1%
ValueCountFrequency (%)
1348117 1
< 0.1%
1013067 1
< 0.1%
885142 1
< 0.1%
750049 1
< 0.1%
708812 1
< 0.1%
695479 1
< 0.1%
631103 1
< 0.1%
630355 1
< 0.1%
551132 1
< 0.1%
501586 1
< 0.1%

predefinedlabel
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.2 KiB
0
6662 
1
6149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12811
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6662
52.0%
1 6149
48.0%

Length

2024-11-25T21:59:06.574673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T21:59:06.665225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6662
52.0%
1 6149
48.0%

Most occurring characters

ValueCountFrequency (%)
0 6662
52.0%
1 6149
48.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12811
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6662
52.0%
1 6149
48.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12811
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6662
52.0%
1 6149
48.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6662
52.0%
1 6149
48.0%

confused
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.2 KiB
1
6567 
0
6244 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12811
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 6567
51.3%
0 6244
48.7%

Length

2024-11-25T21:59:06.760797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T21:59:06.849428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6567
51.3%
0 6244
48.7%

Most occurring characters

ValueCountFrequency (%)
1 6567
51.3%
0 6244
48.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12811
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6567
51.3%
0 6244
48.7%

Most occurring scripts

ValueCountFrequency (%)
Common 12811
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6567
51.3%
0 6244
48.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6567
51.3%
0 6244
48.7%

age
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.2 KiB
24
6394 
25
3819 
28
1314 
31
1284 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters25622
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25
2nd row25
3rd row25
4th row25
5th row25

Common Values

ValueCountFrequency (%)
24 6394
49.9%
25 3819
29.8%
28 1314
 
10.3%
31 1284
 
10.0%

Length

2024-11-25T21:59:06.941838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T21:59:07.044337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
24 6394
49.9%
25 3819
29.8%
28 1314
 
10.3%
31 1284
 
10.0%

Most occurring characters

ValueCountFrequency (%)
2 11527
45.0%
4 6394
25.0%
5 3819
 
14.9%
8 1314
 
5.1%
3 1284
 
5.0%
1 1284
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25622
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 11527
45.0%
4 6394
25.0%
5 3819
 
14.9%
8 1314
 
5.1%
3 1284
 
5.0%
1 1284
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25622
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 11527
45.0%
4 6394
25.0%
5 3819
 
14.9%
8 1314
 
5.1%
3 1284
 
5.0%
1 1284
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25622
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 11527
45.0%
4 6394
25.0%
5 3819
 
14.9%
8 1314
 
5.1%
3 1284
 
5.0%
1 1284
 
5.0%

ethnicity
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.2 KiB
2
10232 
0
1295 
1
1284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12811
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 10232
79.9%
0 1295
 
10.1%
1 1284
 
10.0%

Length

2024-11-25T21:59:07.154137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T21:59:07.242596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 10232
79.9%
0 1295
 
10.1%
1 1284
 
10.0%

Most occurring characters

ValueCountFrequency (%)
2 10232
79.9%
0 1295
 
10.1%
1 1284
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12811
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10232
79.9%
0 1295
 
10.1%
1 1284
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12811
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10232
79.9%
0 1295
 
10.1%
1 1284
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10232
79.9%
0 1295
 
10.1%
1 1284
 
10.0%

gender
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.2 KiB
1
10236 
0
2575 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12811
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10236
79.9%
0 2575
 
20.1%

Length

2024-11-25T21:59:07.350556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T21:59:07.436706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10236
79.9%
0 2575
 
20.1%

Most occurring characters

ValueCountFrequency (%)
1 10236
79.9%
0 2575
 
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12811
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10236
79.9%
0 2575
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12811
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10236
79.9%
0 2575
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10236
79.9%
0 2575
 
20.1%

Interactions

2024-11-25T21:59:01.096374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:43.941724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:45.115466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:47.155705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:48.329546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:49.522448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:50.658749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:52.277052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:53.615021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:55.214874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:56.628789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:58.347176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:59.704687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:01.200299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:44.039894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:45.196052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:47.235730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:48.410578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:49.600763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:50.744726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:52.371357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:53.708744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:55.320996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:56.725193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:58.443368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:59.801638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:01.291611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:44.124805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:45.277755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:47.316983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:48.491905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:49.678998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:50.835427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:52.469420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:53.829045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:55.435321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:56.823285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:58.537097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:59.898804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:01.392798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:44.214942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:45.360599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:47.401614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:48.579818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:49.762486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:50.933285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:52.571527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:53.944978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:55.543657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:56.921528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:58.645149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:00.011670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:01.497529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:44.300316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:45.441858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:47.489672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:48.664953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:49.845786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:51.041198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:52.673944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:54.062688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:55.644634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:57.019117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:58.749519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:00.119057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:01.592175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:44.380446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:45.522430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:47.574290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:48.747392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:49.923687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:51.400657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:52.774618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:54.175105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:55.747309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:57.115161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:58.845291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:00.215643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:01.701336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:44.479416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:45.619712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:47.671735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:48.845806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:50.020974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:51.505537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:52.888796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:54.308769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:55.872777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:57.231110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:58.959099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:00.329949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:01.801959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:44.568599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:46.610458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:47.763277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:48.937457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:50.107220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:51.611264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:52.988790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:54.437755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:55.980799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:57.352927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:59.059147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:00.440901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:01.911154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:44.664380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:46.711277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:47.857907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:49.033595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:50.198824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:51.720397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:53.091008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:54.558768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:56.100031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:57.476968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:59.186875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:00.553346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:02.013330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:44.748838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:46.793541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:47.953191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:49.124066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:50.290645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:51.833257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:53.197843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:54.682678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:56.195467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:57.579787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:59.283328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:00.657083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:02.109036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:44.831046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:46.878094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:48.045416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:49.211399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:50.376660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:51.934800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:53.291662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:54.814069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:56.294910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:57.698062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:59.377581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:00.763969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:02.220058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:44.921293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:46.965944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:48.138584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:49.313848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:50.472733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:52.051435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:53.400743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:54.955417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:56.409066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:57.810945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:59.481114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:00.871586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:02.329983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:45.020617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:47.062266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:48.235870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:49.428529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:50.569084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:52.171459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:53.509034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:55.094483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:56.525144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:58.246823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:58:59.599009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T21:59:00.990029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-25T21:59:07.513946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Alpha1Alpha2AttentionBeta1Beta2DeltaGamma1Gamma2MediationRawSubjectIDThetaVideoIDageconfusedethnicitygenderpredefinedlabel
Alpha11.0000.649-0.2520.6250.5740.6000.5460.457-0.0130.0150.1640.692-0.0010.0560.0840.0690.0500.011
Alpha20.6491.000-0.2670.6680.6460.5640.6100.516-0.0650.0330.1920.6550.0180.0770.0800.0770.0350.017
Attention-0.252-0.2671.000-0.232-0.172-0.298-0.228-0.1660.416-0.057-0.181-0.3740.1280.1880.1780.1810.1050.072
Beta10.6250.668-0.2321.0000.6740.5100.6480.644-0.1950.0360.0980.6580.0180.0510.0720.0500.0270.016
Beta20.5740.646-0.1720.6741.0000.4970.8160.722-0.2940.0310.2100.6010.0050.1050.0120.0700.0440.095
Delta0.6000.564-0.2980.5100.4971.0000.5070.357-0.1670.0180.2520.695-0.0030.1890.1610.2560.1760.014
Gamma10.5460.610-0.2280.6480.8160.5071.0000.785-0.2760.0390.1960.5810.0360.0790.0270.0470.0580.076
Gamma20.4570.516-0.1660.6440.7220.3570.7851.000-0.2620.0560.0970.4870.0420.0530.0240.0250.0450.043
Mediation-0.013-0.0650.416-0.195-0.294-0.167-0.276-0.2621.000-0.045-0.154-0.2740.0340.2090.0790.1790.1030.041
Raw0.0150.033-0.0570.0360.0310.0180.0390.056-0.0451.0000.0090.020-0.0040.2100.0580.1760.1920.033
SubjectID0.1640.192-0.1810.0980.2100.2520.1960.097-0.1540.0091.0000.1890.0021.0000.1401.0001.0000.000
Theta0.6920.655-0.3740.6580.6010.6950.5810.487-0.2740.0200.1891.0000.0080.0850.1270.1120.0450.022
VideoID-0.0010.0180.1280.0180.005-0.0030.0360.0420.034-0.0040.0020.0081.0000.0000.3540.0000.0001.000
age0.0560.0770.1880.0510.1050.1890.0790.0530.2090.2101.0000.0850.0001.0000.0000.7420.7130.000
confused0.0840.0800.1780.0720.0120.1610.0270.0240.0790.0580.1400.1270.3540.0001.0000.0470.0140.021
ethnicity0.0690.0770.1810.0500.0700.2560.0470.0250.1790.1761.0000.1120.0000.7420.0471.0000.2520.000
gender0.0500.0350.1050.0270.0440.1760.0580.0450.1030.1921.0000.0450.0000.7130.0140.2521.0000.000
predefinedlabel0.0110.0170.0720.0160.0950.0140.0760.0430.0410.0330.0000.0221.0000.0000.0210.0000.0001.000

Missing values

2024-11-25T21:59:02.484105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-25T21:59:02.731450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SubjectIDVideoIDAttentionMediationRawDeltaThetaAlpha1Alpha2Beta1Beta2Gamma1Gamma2predefinedlabelconfusedageethnicitygender
00856.043.0278.0301963.090612.033735.023991.027946.045097.033228.08293.0002521
10840.035.0-50.073787.028083.01439.02240.02746.03687.05293.02740.0002521
20847.048.0101.0758353.0383745.0201999.062107.036293.0130536.057243.025354.0002521
30847.057.0-5.02012240.0129350.061236.017084.011488.062462.049960.033932.0002521
40844.053.0-8.01005145.0354328.037102.088881.045307.099603.044790.029749.0002521
50844.066.073.01786446.0176766.059352.026157.015054.033669.033782.031750.0002521
60843.069.0130.0635191.0122446.090107.065072.036230.053019.062938.059307.0002521
70840.061.0-2.0161098.012119.01963.0809.01277.03186.03266.02518.0002521
80843.069.017.0492796.0120998.063697.068242.010769.088403.073756.022676.0002521
90847.069.0-59.082048.0116131.047317.026197.041642.028866.032551.041810.0002521
SubjectIDVideoIDAttentionMediationRawDeltaThetaAlpha1Alpha2Beta1Beta2Gamma1Gamma2predefinedlabelconfusedageethnicitygender
128019453.061.0474.0667793.014829.02344.09768.02393.07217.08036.0951.0102420
128029464.061.0397.0384069.044069.015317.017620.04313.053160.018216.01006.0102420
128039451.063.0-58.02045793.0178968.0151240.017596.022894.045205.017791.02711.0102420
128049451.063.0-45.046525.07099.01830.01882.0279.01051.01674.089.0102420
128059454.041.0305.0313623.053046.01403.05641.02010.013052.03209.0519.0102420
128069464.038.0-39.0127574.09951.0709.021732.03872.039728.02598.0960.0102420
128079461.035.0-275.0323061.0797464.0153171.0145805.039829.0571280.036574.010010.0102420
128089460.029.0-426.0680989.0154296.040068.039122.010966.026975.020427.02024.0102420
128099460.029.0-84.0366269.027346.011444.09932.01939.03283.012323.01764.0102420
128109464.029.0-49.01164555.01184366.050014.0124208.010634.0445383.022133.04482.0102420